TV/Reading

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library(ggplot2)
library(tidyr)

# Create figure directory at ../../figures
figures_dir <- file.path("..", "..", "figures")
if (!dir.exists(figures_dir)) {
    dir.create(figures_dir, recursive = TRUE)
}

Loading data from nda3.0.Rds file & plink2.eigenvec file

file_path <- file.path("..", "..", "data", "plink2.eigenvec")
genomic_data <- read.table(file_path, header = TRUE)
cat("Number of lines in the file: ", nrow(data), "\n")

Number of lines in the file:  

file_path <- file.path("..", "..", "data", "nda3.0.Rds")
data <- readRDS(file_path)

Warning: input string 'Compañía de Construcion' cannot be translated from 'ANSI_X3.4-1968' to UTF-8, but is valid UTF-8Warning: input string 'Compañia de limpieza' cannot be translated from 'ANSI_X3.4-1968' to UTF-8, but is valid UTF-8Warning: input string 'Compañia de limpieza- PMS' cannot be translated from 'ANSI_X3.4-1968' to UTF-8, but is valid UTF-8Warning: input string 'Demolición, ' cannot be translated from 'ANSI_X3.4-1968' to UTF-8, but is valid UTF-8Warning: input string 'Mom❤️😘' cannot be translated from 'ANSI_X3.4-1968' to UTF-8, but is valid UTF-8Warning: input string 'Grandma 👵 ' cannot be translated from 'ANSI_X3.4-1968' to UTF-8, but is valid UTF-8Warning: input string 'Her own 👟 ' cannot be translated from 'ANSI_X3.4-1968' to UTF-8, but is valid UTF-8Warning: input string 'Her own 👟 ' cannot be translated from 'ANSI_X3.4-1968' to UTF-8, but is valid UTF-8

# Filter for baseline data only
data <- dplyr::filter(data, eventname == "baseline_year_1_arm_1")
cat("Number of lines after filtering by eventname: ", nrow(data), "\n")

Number of lines after filtering by eventname:  11878 

# Drop the `eventname` column
#data <- dplyr::select(data, -eventname)

This takes quite a while — we will work with a subset of the data in the rest of this notebook.

Variable exploration

TV watching variable exploration

Two variables seem similar: screentime_wkdy_1 and screentime_wkdy_typical_hr.

data_filtered <- dplyr::select(data2, src_subject_id, screentime_wkdy_1, screentime_wkdy_typical_hr)

data_filtered <- data_filtered[!(is.na(data_filtered$screentime_wkdy_1) & is.na(data_filtered$screentime_wkdy_typical_hr)), ]
# Print the number of rows removed
cat("Number of rows after removal where both columns are NA:", nrow(data_filtered), "\n")

# Check how many rows remain with NA in either column
na_screentime_1 <- is.na(data_filtered$screentime_wkdy_1)
na_screentime_typical <- is.na(data_filtered$screentime_wkdy_typical_hr)

na_overlap <- sum(na_screentime_1 & na_screentime_typical)
na_in_one <- sum(na_screentime_1 | na_screentime_typical)

# Number of NAs in each column individually
na_screentime_1_count <- sum(na_screentime_1)
na_screentime_typical_count <- sum(na_screentime_typical)

cat("Number of NAs in screentime_wkdy_1:", na_screentime_1_count, "\n")
cat("Number of NAs in screentime_wkdy_typical_hr:", na_screentime_typical_count, "\n")

# Print remaining NA analysis
cat("Number of rows where both columns are NA (after cleaning):", na_overlap, "\n")
cat("Number of rows where at least one column is NA (after cleaning):", na_in_one, "\n")

It seems we only need the first one, screentime_wkdy_1. We will add the equivalent for the weekend screen time, screentime_wknd_7. For reading, we use sports_activity_ss_read_hours_p.

Get subset of data and summarize variables

column_names <- names(data)
search_columns <- function(search_string, column_names) {
    # Perform regex search
    matching_columns <- grep(search_string, column_names, value = TRUE)
    return(matching_columns)
}
demographic_variables <- c("interview_age", "sex", "abcd_site", "mri_info_device.serial.number", 
                  "married.bl", "household.income.bl", "high.educ.bl", "hisp", "rel_family_id")
phenotype_variables <- c("sports_activity_ss_read_hours_p",
                         "cbcl_scr_dsm5_adhd_t",
                         "screentime_wkdy_1",
                         "screentime_wknd_7"
                         )
nih_scores <- search_columns("nihtbx.*uncorrected", names(data))
quality_control_variables <- c("mrif_score", "fsqc_qc")
imaging_tabulated_variables <- search_columns("smri_(thick|area).*desikan", column_names)
cat("Number of imaging variables found: ", length(imaging_tabulated_variables), "\n")

Number of imaging variables found:  142 

# Select relevant columns with the above lists
data_subset <- dplyr::select(data,
                             src_subject_id, eventname,
                             all_of(demographic_variables),
                             all_of(phenotype_variables),
                             all_of(nih_scores),
                             all_of(quality_control_variables),
                             all_of(imaging_tabulated_variables))
cat("\nMatrix size after column selection: ", dim(data_subset), "\n")

Matrix size after column selection:  11878 169 

# Merge data_subset with genomic_data on src_subject_id
data_subset <- dplyr::left_join(data_subset, genomic_data, by = c("src_subject_id" = "IID"))
data_subset <- dplyr::select(data_subset, -FID)
cat("Matrix size after merging with genomic data: ", dim(data_subset), "\n")

Matrix size after merging with genomic data:  11878 189 

data_subset <- dplyr::filter(data_subset, fsqc_qc == "accept")
cat("Number of lines after filtering by fsqc_qc: ", nrow(data_subset), "\n") 

Number of lines after filtering by fsqc_qc:  11265 

data_subset <- dplyr::filter(data_subset, mrif_score == "No abnormal findings" | mrif_score == "Normal anatomical variant of no clinical significance")
cat("Number of lines after filtering by mrif_score: ", nrow(data_subset), "\n")

Number of lines after filtering by mrif_score:  10783 

# Filter NAs in all variables in "phenotype_variables"
for (variable in phenotype_variables) {
    if (grepl("cbcl", variable)) {
        next
    }
    data_subset <- dplyr::filter(data_subset, !is.na(data_subset[[variable]]))
    cat("Number of lines after filtering NAs in", variable, ":", nrow(data_subset), "\n")
}

Number of lines after filtering NAs in sports_activity_ss_read_hours_p : 10038 
Number of lines after filtering NAs in screentime_wkdy_1 : 10022 
Number of lines after filtering NAs in screentime_wknd_7 : 10017 

# Filter missing reading data
data_subset <- dplyr::filter(data_subset, !is.na(sports_activity_ss_read_hours_p))
cat("Number of lines after filtering missing reading data: ", nrow(data_subset), "\n")

Number of lines after filtering missing reading data:  10017 

# Filter cases where reading is above 56 hours
data_subset <- dplyr::filter(data_subset, sports_activity_ss_read_hours_p <= 56)
cat("Number of lines after filtering by reading values above 56: ", nrow(data_subset), "\n")

Number of lines after filtering by reading values above 56:  9968 

# Filter missing imaging data
for (variable in imaging_tabulated_variables) {
    data_subset <- dplyr::filter(data_subset, !is.na(data_subset[[variable]]))
}
cat("Number of lines after filtering NAs in tabulated imaging data:", nrow(data_subset), "\n")

Number of lines after filtering NAs in tabulated imaging data: 9965 

# Filter missing demographic data
for (variable in demographic_variables) {
    data_subset <- dplyr::filter(data_subset, !is.na(data_subset[[variable]]))
    cat("Number of lines after filtering NAs in", variable, ":", nrow(data_subset), "\n")
}

Number of lines after filtering NAs in interview_age : 9965 
Number of lines after filtering NAs in sex : 9965 
Number of lines after filtering NAs in abcd_site : 9965 
Number of lines after filtering NAs in mri_info_device.serial.number : 9954 
Number of lines after filtering NAs in married.bl : 9881 
Number of lines after filtering NAs in household.income.bl : 9118 
Number of lines after filtering NAs in high.educ.bl : 9114 
Number of lines after filtering NAs in hisp : 9013 
Number of lines after filtering NAs in rel_family_id : 9013 

# Filter missing genomic data
data_subset <- dplyr::filter(data_subset, !is.na(PC1))
cat("Number of lines after filtering NAs in genomic data: ", nrow(data_subset), "\n")

Number of lines after filtering NAs in genomic data:  8127 

# # Filter missing NIH scores (only done for Figure 1)
# for (variable in c("nihtbx_fluidcomp_uncorrected", "nihtbx_cryst_uncorrected", "nihtbx_totalcomp_uncorrected")) {
#     data_subset <- dplyr::filter(data_subset, !is.na(data_subset[[variable]]))
#     cat("Number of lines after filtering NAs in", variable, ":", nrow(data_subset), "\n")
# }

# “So it's 11,875 total > 11,810 (missing imaging data) > 10738 (imaging QC) > 10017 (missing behavioral data) > 9,968 (outlier filtering)”
#“I can confirm that imaging QC, outlier filtering, and missing behavioral data yields 9,968 subjects but after running DEAPext the final analysis consists of 8,125”
#“Thanks to Pierre's efforts we figured out that the drop from the 9000s to 8000s post-analysis is surprisingly from missing demographic data (most prominently household income and hispanic ethnicity but others as well). My pre-filtering steps only filtered for missing behavioral data which is why there was a discrepancy that only was revealed post-analysis.”


# Create screentime variable
# the screetime_kday/wdnd are levels: [ "None"       "15 minutes" "30 minutes" "1 hour"     "2 hours"    "3 hours"    "4+ hours"  ] --> convert to pseudo continuous!
# Convert screentime levels to numeric values
screentime_levels <- c("None" = 0, "15 minutes" = 0.25, "30 minutes" = 0.5, "1 hour" = 1, "2 hours" = 2, "3 hours" = 3, "4+ hours" = 4)
data_subset$screentime_wkdy_1_num <- as.numeric(screentime_levels[data_subset$screentime_wkdy_1])
data_subset$screentime_wknd_7_num <- as.numeric(screentime_levels[data_subset$screentime_wknd_7])
data_subset$screentime <- (data_subset$screentime_wkdy_1_num * 5 + data_subset$screentime_wknd_7_num * 2) / 7

# Create the daily reading time variable
data_subset$readtime <- data_subset$sports_activity_ss_read_hours_p / 7

# Summarize each variable
for (variable in colnames(data_subset)) {
    if (variable != "src_subject_id") {
        cat("\nSummary for variable: ", variable, "\n")
        print(summary(data_subset[[variable]]))
    }
}

Summary for variable:  eventname 
1_year_follow_up_y_arm_1 2_year_follow_up_y_arm_1    baseline_year_1_arm_1 18_month_follow_up_arm_1 30_month_follow_up_arm_1  6_month_follow_up_arm_1 
                       0                        0                     8127                        0                        0                        0 

Summary for variable:  interview_age 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  107.0   112.0   119.0   119.1   126.0   132.0 

Summary for variable:  sex 
   F    M 
3855 4272 

Summary for variable:  abcd_site 
       site01 site02 site03 site04 site05 site06 site07 site08 site09 site10 site11 site12 site13 site14 site15 site16 site17 site18 site19 site20 site21 site22 
     0    233    428    418    458    261    450    230    244    296    471    313    357    438    476    268    820    416    281    367    520    361     21 

Summary for variable:  mri_info_device.serial.number 
             HASH03db707f HASH11ad4ed5 HASH1314a204 HASH311170b9 HASH31ce566d HASH3935c89e HASH4036a433 HASH48f7cbc3 HASH4b0b8b05 HASH4d1ed7b1 HASH5ac2b20b HASH5b0cf1bb HASH5b2fcf80 
         156          304          361          400          260           35          805            0           22          291          294          366          413          241 
HASH65b39280 HASH69f406fa HASH6b4422a7 HASH7911780b HASH7f91147d HASH96a0c182 HASHa3e45734 HASHb640a1b8 HASHc3bf3d9c HASHc9398971 HASHd422be27 HASHd7cb4c6d HASHdb2589d4 HASHe3ce02d3 
         222           98          233          262           68          440          281          337          343          186          326          385          417           86 
HASHe4f6957a HASHe76e6d72 HASHfeb7e81a 
         313           16          166 

Summary for variable:  married.bl 
  no  yes 
2318 5809 

Summary for variable:  household.income.bl 
         [<50K] [>=50K & <100K]        [>=100K] 
           2157            2365            3605 

Summary for variable:  high.educ.bl 
        < HS Diploma       HS Diploma/GED         Some College             Bachelor Post Graduate Degree 
                 279                  594                 1996                 2224                 3034 

Summary for variable:  hisp 
  No  Yes 
6563 1564 

Summary for variable:  rel_family_id 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      3    3004    5926    5948    8928   11881 

Summary for variable:  sports_activity_ss_read_hours_p 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.000   3.000   4.227   6.000  50.000 

Summary for variable:  cbcl_scr_dsm5_adhd_t 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  50.00   50.00   50.00   53.11   55.00   80.00       1 

Summary for variable:  screentime_wkdy_1 
      None       0.25 30 minutes     1 hour    2 hours    3 hours   4+ hours 
      1050       1029       1903       2092       1104        463        486 

Summary for variable:  screentime_wknd_7 
        None < 30 minutes   30 minutes       1 hour      2 hours      3 hours     4+ hours 
         529          637         1169         2231         1690          818         1053 

Summary for variable:  nihtbx_picvocab_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  36.00   80.00   85.00   85.21   90.00  119.00      95 

Summary for variable:  nihtbx_flanker_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  54.00   90.00   96.00   94.47  101.00  116.00      99 

Summary for variable:  nihtbx_list_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  36.00   90.00   97.00   97.63  105.00  136.00     123 

Summary for variable:  nihtbx_cardsort_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  50.00   88.00   94.00   93.08   99.00  120.00      97 

Summary for variable:  nihtbx_pattern_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  36.00   80.00   88.00   88.37   99.00  140.00     111 

Summary for variable:  nihtbx_picture_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   76.0    95.0   103.0   103.4   112.0   136.0     101 

Summary for variable:  nihtbx_reading_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  63.00   88.00   91.00   91.28   95.00  119.00     103 

Summary for variable:  nihtbx_fluidcomp_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  44.00   86.00   93.00   92.35   99.00  131.00     150 

Summary for variable:  nihtbx_cryst_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  59.00   83.00   87.00   86.99   91.00  115.00     115 

Summary for variable:  nihtbx_totalcomp_uncorrected 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  46.00   82.00   88.00   87.06   93.00  117.00     154 

Summary for variable:  mrif_score 
               Image artifacts prevent radiology read                                  No abnormal findings Normal anatomical variant of no clinical significance 
                                                    0                                                  6723                                                  1404 
                           Consider clinical referral                  Consider immediate clinical referral 
                                                    0                                                     0 

Summary for variable:  fsqc_qc 
reject accept 
     0   8127 

Summary for variable:  smri_area_cort.desikan_bankssts.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    616    1063    1189    1200    1323    2029 

Summary for variable:  smri_area_cort.desikan_bankssts.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    596     993    1099    1110    1214    1843 

Summary for variable:  smri_area_cort.desikan_caudalanteriorcingulate.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  357.0   630.0   718.0   743.6   833.0  1598.0 

Summary for variable:  smri_area_cort.desikan_caudalanteriorcingulate.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  377.0   739.0   847.0   863.4   972.0  1722.0 

Summary for variable:  smri_area_cort.desikan_caudalmiddlefrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1146    2336    2640    2660    2957    4398 

Summary for variable:  smri_area_cort.desikan_caudalmiddlefrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    985    2122    2423    2451    2754    4856 

Summary for variable:  smri_area_cort.desikan_cuneus.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    590    1430    1563    1565    1703    2670 

Summary for variable:  smri_area_cort.desikan_cuneus.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    540    1473    1612    1606    1753    2581 

Summary for variable:  smri_area_cort.desikan_entorhinal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  175.0   359.0   412.0   419.5   470.0   992.0 

Summary for variable:  smri_area_cort.desikan_entorhinal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  144.0   301.0   356.0   365.3   419.0  1029.0 

Summary for variable:  smri_area_cort.desikan_frontalpole.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   68.0   180.0   204.0   205.3   229.0   400.0 

Summary for variable:  smri_area_cort.desikan_frontalpole.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  102.0   257.0   290.0   292.2   324.0   555.0 

Summary for variable:  smri_area_cort.desikan_fusiform.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2091    3270    3558    3579    3870    5822 

Summary for variable:  smri_area_cort.desikan_fusiform.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2000    3192    3477    3498    3793    5230 

Summary for variable:  smri_area_cort.desikan_inferiorparietal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2881    4732    5200    5251    5722    8900 

Summary for variable:  smri_area_cort.desikan_inferiorparietal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3668    5696    6247    6288    6833    9975 

Summary for variable:  smri_area_cort.desikan_inferiortemporal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1962    3285    3635    3657    4004    5968 

Summary for variable:  smri_area_cort.desikan_inferiortemporal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1772    3129    3451    3458    3780    5395 

Summary for variable:  smri_area_cort.desikan_insula.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1532    2102    2255    2275    2427    4358 

Summary for variable:  smri_area_cort.desikan_insula.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1507    2091    2258    2280    2450    3804 

Summary for variable:  smri_area_cort.desikan_isthmuscingulate.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    545     970    1093    1116    1236    2911 

Summary for variable:  smri_area_cort.desikan_isthmuscingulate.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    560     916    1021    1039    1142    4212 

Summary for variable:  smri_area_cort.desikan_lateraloccipital.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3053    4687    5122    5151    5575    8764 

Summary for variable:  smri_area_cort.desikan_lateraloccipital.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2733    4553    4983    5009    5429    7879 

Summary for variable:  smri_area_cort.desikan_lateralorbitofrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1817    2617    2821    2824    3028    4227 

Summary for variable:  smri_area_cort.desikan_lateralorbitofrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1703    2537    2747    2762    2973    4357 

Summary for variable:  smri_area_cort.desikan_lingual.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2099    3072    3344    3358    3628    5254 

Summary for variable:  smri_area_cort.desikan_lingual.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1777    3069    3328    3350    3615    5346 

Summary for variable:  smri_area_cort.desikan_medialorbitofrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1059    1661    1823    1839    2002    3016 

Summary for variable:  smri_area_cort.desikan_medialorbitofrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1149    1730    1873    1880    2023    2669 

Summary for variable:  smri_area_cort.desikan_middletemporal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1947    3111    3394    3422    3712    5668 

Summary for variable:  smri_area_cort.desikan_middletemporal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2238    3457    3763    3791    4106    6275 

Summary for variable:  smri_area_cort.desikan_paracentral.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    885    1274    1398    1422    1548    2646 

Summary for variable:  smri_area_cort.desikan_paracentral.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1031    1446    1603    1631    1781    3632 

Summary for variable:  smri_area_cort.desikan_parahippocampal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  414.0   680.0   752.0   767.4   834.0  2615.0 

Summary for variable:  smri_area_cort.desikan_parahippocampal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  422.0   661.0   731.0   746.0   813.5  2703.0 

Summary for variable:  smri_area_cort.desikan_parsopercularis.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1079    1626    1819    1847    2038    3461 

Summary for variable:  smri_area_cort.desikan_parsopercularis.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    843    1354    1525    1553    1723    2806 

Summary for variable:  smri_area_cort.desikan_parsorbitalis.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  306.0   612.0   671.0   673.2   732.0  1044.0 

Summary for variable:  smri_area_cort.desikan_parsorbitalis.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    471     756     832     834     906    1268 

Summary for variable:  smri_area_cort.desikan_parstriangularis.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    806    1287    1428    1441    1584    2550 

Summary for variable:  smri_area_cort.desikan_parstriangularis.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    856    1496    1676    1691    1869    2784 

Summary for variable:  smri_area_cort.desikan_pericalcarine.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    734    1342    1496    1504    1662    2598 

Summary for variable:  smri_area_cort.desikan_pericalcarine.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    652    1482    1644    1643    1809    2525 

Summary for variable:  smri_area_cort.desikan_postcentral.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2209    4163    4515    4572    4930    7847 

Summary for variable:  smri_area_cort.desikan_postcentral.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2056    3954    4300    4344    4692    7361 

Summary for variable:  smri_area_cort.desikan_posteriorcingulate.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    788    1180    1308    1330    1456    2575 

Summary for variable:  smri_area_cort.desikan_posteriorcingulate.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    752    1211    1351    1374    1506    2894 

Summary for variable:  smri_area_cort.desikan_precentral.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3293    4705    5080    5137    5510    9186 

Summary for variable:  smri_area_cort.desikan_precentral.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3288    4745    5136    5194    5569    9248 

Summary for variable:  smri_area_cort.desikan_precuneus.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2455    3840    4195    4210    4557    6470 

Summary for variable:  smri_area_cort.desikan_precuneus.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2356    4020    4405    4426    4812    6759 

Summary for variable:  smri_area_cort.desikan_rostralanteriorcingulate.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  346.0   784.0   892.0   897.7  1003.0  1604.0 

Summary for variable:  smri_area_cort.desikan_rostralanteriorcingulate.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  334.0   645.0   737.0   743.8   835.0  1337.0 

Summary for variable:  smri_area_cort.desikan_rostralmiddlefrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3730    5944    6507    6547    7087   10588 

Summary for variable:  smri_area_cort.desikan_rostralmiddlefrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   4003    6140    6714    6777    7348   11975 

Summary for variable:  smri_area_cort.desikan_superiorfrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   4924    7322    7944    7995    8595   12349 

Summary for variable:  smri_area_cort.desikan_superiorfrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   4852    7087    7710    7766    8379   12218 

Summary for variable:  smri_area_cort.desikan_superiorparietal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3428    5480    5956    6003    6489    9794 

Summary for variable:  smri_area_cort.desikan_superiorparietal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3443    5523    5993    6033    6504    9138 

Summary for variable:  smri_area_cort.desikan_superiortemporal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2601    3784    4112    4140    4469    6635 

Summary for variable:  smri_area_cort.desikan_superiortemporal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2601    3616    3896    3928    4212    6193 

Summary for variable:  smri_area_cort.desikan_supramarginal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2112    3948    4371    4428    4850    8018 

Summary for variable:  smri_area_cort.desikan_supramarginal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2177    3702    4099    4153    4552    7199 

Summary for variable:  smri_area_cort.desikan_temporalpole.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    259     427     467     470     511     767 

Summary for variable:  smri_area_cort.desikan_temporalpole.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    177     369     406     411     449     706 

Summary for variable:  smri_area_cort.desikan_total 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 129626  174368  186157  186792  198481  259003 

Summary for variable:  smri_area_cort.desikan_total.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  64276   86920   92840   93140   99020  131154 

Summary for variable:  smri_area_cort.desikan_total.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  65323   87379   93319   93652   99528  130937 

Summary for variable:  smri_area_cort.desikan_transversetemporal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  238.0   432.0   481.0   488.4   537.0   848.0 

Summary for variable:  smri_area_cort.desikan_transversetemporal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    182     320     356     361     397     667 

Summary for variable:  smri_thick_cort.desikan_bankssts.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.997   2.698   2.808   2.807   2.917   3.509 

Summary for variable:  smri_thick_cort.desikan_bankssts.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.199   2.783   2.899   2.899   3.016   3.604 

Summary for variable:  smri_thick_cort.desikan_caudalanteriorcingulate.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.997   2.723   2.867   2.880   3.030   4.023 

Summary for variable:  smri_thick_cort.desikan_caudalanteriorcingulate.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.000   2.606   2.731   2.744   2.865   3.744 

Summary for variable:  smri_thick_cort.desikan_caudalmiddlefrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.055   2.789   2.882   2.876   2.972   3.484 

Summary for variable:  smri_thick_cort.desikan_caudalmiddlefrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.108   2.759   2.854   2.846   2.944   3.349 

Summary for variable:  smri_thick_cort.desikan_cuneus.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.423   1.957   2.057   2.061   2.161   2.753 

Summary for variable:  smri_thick_cort.desikan_cuneus.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.447   1.977   2.082   2.082   2.191   2.765 

Summary for variable:  smri_thick_cort.desikan_entorhinal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.895   3.245   3.454   3.451   3.661   4.606 

Summary for variable:  smri_thick_cort.desikan_entorhinal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.621   3.347   3.586   3.573   3.825   4.648 

Summary for variable:  smri_thick_cort.desikan_frontalpole.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.443   3.008   3.212   3.211   3.419   4.735 

Summary for variable:  smri_thick_cort.desikan_frontalpole.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.433   2.983   3.168   3.173   3.369   4.415 

Summary for variable:  smri_thick_cort.desikan_fusiform.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.435   2.883   2.965   2.963   3.045   3.417 

Summary for variable:  smri_thick_cort.desikan_fusiform.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.269   2.885   2.968   2.966   3.051   3.409 

Summary for variable:  smri_thick_cort.desikan_inferiorparietal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.066   2.693   2.797   2.781   2.883   3.266 

Summary for variable:  smri_thick_cort.desikan_inferiorparietal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.140   2.727   2.825   2.809   2.908   3.268 

Summary for variable:  smri_thick_cort.desikan_inferiortemporal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.264   2.984   3.088   3.082   3.187   3.634 

Summary for variable:  smri_thick_cort.desikan_inferiortemporal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.249   3.012   3.115   3.106   3.212   3.666 

Summary for variable:  smri_thick_cort.desikan_insula.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.699   3.215   3.308   3.305   3.397   3.740 

Summary for variable:  smri_thick_cort.desikan_insula.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.646   3.197   3.298   3.296   3.399   3.835 

Summary for variable:  smri_thick_cort.desikan_isthmuscingulate.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.081   2.571   2.690   2.699   2.813   3.532 

Summary for variable:  smri_thick_cort.desikan_isthmuscingulate.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.106   2.526   2.640   2.651   2.762   3.513 

Summary for variable:  smri_thick_cort.desikan_lateraloccipital.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.811   2.233   2.342   2.334   2.439   2.910 

Summary for variable:  smri_thick_cort.desikan_lateraloccipital.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.807   2.294   2.405   2.395   2.505   2.936 

Summary for variable:  smri_thick_cort.desikan_lateralorbitofrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.099   2.892   2.992   2.990   3.091   3.566 

Summary for variable:  smri_thick_cort.desikan_lateralorbitofrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.907   2.863   2.970   2.965   3.069   3.510 

Summary for variable:  smri_thick_cort.desikan_lingual.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.663   2.107   2.197   2.198   2.287   2.720 

Summary for variable:  smri_thick_cort.desikan_lingual.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.724   2.144   2.232   2.235   2.326   2.754 

Summary for variable:  smri_thick_cort.desikan_mean 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.367   2.713   2.786   2.780   2.850   3.140 

Summary for variable:  smri_thick_cort.desikan_mean.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.337   2.717   2.789   2.783   2.855   3.149 

Summary for variable:  smri_thick_cort.desikan_mean.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.351   2.710   2.782   2.777   2.847   3.130 

Summary for variable:  smri_thick_cort.desikan_medialorbitofrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.823   2.615   2.728   2.729   2.840   3.366 

Summary for variable:  smri_thick_cort.desikan_medialorbitofrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.019   2.637   2.754   2.752   2.868   3.440 

Summary for variable:  smri_thick_cort.desikan_middletemporal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.211   3.098   3.213   3.193   3.314   3.798 

Summary for variable:  smri_thick_cort.desikan_middletemporal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.209   3.117   3.236   3.213   3.336   3.854 

Summary for variable:  smri_thick_cort.desikan_paracentral.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.084   2.649   2.753   2.750   2.852   3.386 

Summary for variable:  smri_thick_cort.desikan_paracentral.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.156   2.657   2.753   2.751   2.845   3.282 

Summary for variable:  smri_thick_cort.desikan_parahippocampal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.108   2.803   3.002   2.999   3.193   4.325 

Summary for variable:  smri_thick_cort.desikan_parahippocampal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.026   2.791   2.954   2.954   3.116   3.941 

Summary for variable:  smri_thick_cort.desikan_parsopercularis.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.216   2.830   2.920   2.916   3.006   3.475 

Summary for variable:  smri_thick_cort.desikan_parsopercularis.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.241   2.810   2.908   2.907   3.002   3.441 

Summary for variable:  smri_thick_cort.desikan_parsorbitalis.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.205   2.996   3.130   3.130   3.265   3.971 

Summary for variable:  smri_thick_cort.desikan_parsorbitalis.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.225   2.967   3.101   3.102   3.237   4.117 

Summary for variable:  smri_thick_cort.desikan_parstriangularis.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.161   2.725   2.825   2.823   2.925   3.424 

Summary for variable:  smri_thick_cort.desikan_parstriangularis.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.740   2.707   2.809   2.805   2.911   3.506 

Summary for variable:  smri_thick_cort.desikan_pericalcarine.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.287   1.658   1.758   1.762   1.857   2.491 

Summary for variable:  smri_thick_cort.desikan_pericalcarine.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.288   1.651   1.752   1.755   1.852   2.366 

Summary for variable:  smri_thick_cort.desikan_postcentral.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.778   2.229   2.330   2.331   2.434   3.055 

Summary for variable:  smri_thick_cort.desikan_postcentral.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.746   2.195   2.298   2.301   2.404   2.976 

Summary for variable:  smri_thick_cort.desikan_posteriorcingulate.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.228   2.671   2.765   2.773   2.868   3.549 

Summary for variable:  smri_thick_cort.desikan_posteriorcingulate.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.038   2.638   2.721   2.729   2.810   3.388 

Summary for variable:  smri_thick_cort.desikan_precentral.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.812   2.716   2.811   2.798   2.895   3.241 

Summary for variable:  smri_thick_cort.desikan_precentral.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.918   2.683   2.775   2.762   2.855   3.257 

Summary for variable:  smri_thick_cort.desikan_precuneus.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.193   2.631   2.715   2.713   2.797   3.182 

Summary for variable:  smri_thick_cort.desikan_precuneus.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.008   2.642   2.726   2.721   2.806   3.179 

Summary for variable:  smri_thick_cort.desikan_rostralanteriorcingulate.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.229   3.035   3.169   3.168   3.306   4.135 

Summary for variable:  smri_thick_cort.desikan_rostralanteriorcingulate.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.092   2.920   3.062   3.062   3.200   3.899 

Summary for variable:  smri_thick_cort.desikan_rostralmiddlefrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.919   2.647   2.745   2.733   2.833   3.208 

Summary for variable:  smri_thick_cort.desikan_rostralmiddlefrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.962   2.600   2.695   2.688   2.789   3.200 

Summary for variable:  smri_thick_cort.desikan_superiorfrontal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.483   3.047   3.147   3.142   3.242   3.767 

Summary for variable:  smri_thick_cort.desikan_superiorfrontal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.509   3.012   3.104   3.103   3.198   3.766 

Summary for variable:  smri_thick_cort.desikan_superiorparietal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.847   2.410   2.503   2.494   2.588   2.967 

Summary for variable:  smri_thick_cort.desikan_superiorparietal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.898   2.411   2.504   2.495   2.588   3.073 

Summary for variable:  smri_thick_cort.desikan_superiortemporal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.344   3.004   3.121   3.111   3.228   3.680 

Summary for variable:  smri_thick_cort.desikan_superiortemporal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.339   3.027   3.132   3.129   3.236   3.674 

Summary for variable:  smri_thick_cort.desikan_supramarginal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.114   2.779   2.884   2.868   2.979   3.350 

Summary for variable:  smri_thick_cort.desikan_supramarginal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.107   2.769   2.890   2.863   2.982   3.337 

Summary for variable:  smri_thick_cort.desikan_temporalpole.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.831   3.640   3.822   3.811   4.002   4.734 

Summary for variable:  smri_thick_cort.desikan_temporalpole.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.358   3.760   3.963   3.942   4.151   4.747 

Summary for variable:  smri_thick_cort.desikan_transversetemporal.lh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.905   2.616   2.753   2.754   2.889   3.731 

Summary for variable:  smri_thick_cort.desikan_transversetemporal.rh 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.987   2.643   2.777   2.776   2.910   3.602 

Summary for variable:  PC1 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.0064216 -0.0057921 -0.0053010 -0.0006869 -0.0017259  0.0296915 

Summary for variable:  PC2 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.0438964  0.0015891  0.0047200  0.0004142  0.0053848  0.0071839 

Summary for variable:  PC3 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.0327854 -0.0006484  0.0001592  0.0001899  0.0008417  0.0539392 

Summary for variable:  PC4 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-5.063e-03 -1.321e-03 -8.795e-04  4.674e-05 -3.946e-04  2.706e-01 

Summary for variable:  PC5 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-6.119e-02 -1.618e-03  2.765e-03  1.377e-05  6.196e-03  1.614e-02 

Summary for variable:  PC6 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.0554863 -0.0037522  0.0011844  0.0000564  0.0054741  0.0288450 

Summary for variable:  PC7 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.0922031 -0.0067865 -0.0017109 -0.0001939  0.0046775  0.0427558 

Summary for variable:  PC8 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.5157770 -0.0017237 -0.0000645  0.0000139  0.0014557  0.3234070 

Summary for variable:  PC9 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-7.875e-02 -6.520e-03 -4.363e-04  6.124e-05  5.852e-03  1.354e-01 

Summary for variable:  PC10 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.1632660 -0.0021802  0.0005472 -0.0000082  0.0032797  0.4396320 

Summary for variable:  PC11 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-2.753e-01 -5.834e-03  1.820e-04  9.679e-05  6.103e-03  8.028e-02 

Summary for variable:  PC12 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.4443500 -0.0013099 -0.0000604  0.0000175  0.0012488  0.5326450 

Summary for variable:  PC13 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.3194340 -0.0026527  0.0000338 -0.0000508  0.0027588  0.2139670 

Summary for variable:  PC14 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-2.183e-01 -5.278e-03 -1.686e-04 -1.528e-05  4.622e-03  1.429e-01 

Summary for variable:  PC15 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-7.143e-02 -5.418e-03 -1.091e-04 -1.742e-05  5.233e-03  7.992e-02 

Summary for variable:  PC16 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.3672920 -0.0027239  0.0000621  0.0000509  0.0029866  0.2636760 

Summary for variable:  PC17 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-0.3897230 -0.0021331 -0.0002144  0.0000662  0.0017630  0.2317530 

Summary for variable:  PC18 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-8.897e-02 -5.178e-03  2.432e-04 -7.027e-05  5.797e-03  6.453e-02 

Summary for variable:  PC19 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-1.319e-01 -4.848e-03  2.740e-04 -2.388e-05  5.200e-03  1.277e-01 

Summary for variable:  PC20 
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-3.042e-01 -3.843e-03 -3.923e-05 -2.761e-05  3.650e-03  9.395e-02 

Summary for variable:  screentime_wkdy_1_num 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.250   1.000   1.088   2.000   4.000 

Summary for variable:  screentime_wknd_7_num 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.500   1.000   1.602   2.000   4.000 

Summary for variable:  screentime 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.500   1.000   1.235   1.714   4.000 

Summary for variable:  readtime 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.4286  0.6038  0.8571  7.1429 

Visualize the data

Individual variables

data_filtered <- data_subset
# Summary statistics for sports_activity_ss_read_hours_p
cat("Summary statistics for sports_activity_ss_read_hours_p: \n")

Summary statistics for sports_activity_ss_read_hours_p: 

summary(data_filtered$sports_activity_ss_read_hours_p)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   0.000   3.000   4.227   6.000  50.000 

# Summary statistics for screentime variables
cat("Summary statistics for screentime_wkdy_1: \n")

Summary statistics for screentime_wkdy_1: 

summary(data_filtered$screentime_wkdy_1)

      None       0.25 30 minutes     1 hour    2 hours    3 hours   4+ hours 
      1050       1029       1903       2092       1104        463        486 

cat("Summary statistics for screentime_wknd_7: \n")

Summary statistics for screentime_wknd_7: 

summary(data_filtered$screentime_wknd_7)

        None < 30 minutes   30 minutes       1 hour      2 hours      3 hours     4+ hours 
         529          637         1169         2231         1690          818         1053 

## Visualize the distribution of sports_activity_ss_read_hours_p with log scale on y-axis

# Calculate the number of cases in each category
zero_to_eight <- sum(data$sports_activity_ss_read_hours_p / 7 <= 8, na.rm = TRUE)
eight_to_fourteen <- sum(data$sports_activity_ss_read_hours_p / 7 > 8 & data$sports_activity_ss_read_hours_p / 7 <= 14, na.rm = TRUE)
four_to_eight <- sum(data$sports_activity_ss_read_hours_p / 7 > 4 & data$sports_activity_ss_read_hours_p / 7 <= 8, na.rm = TRUE)
more_than_fourteen <- sum(data$sports_activity_ss_read_hours_p / 7 > 14, na.rm = TRUE)
cat("Number of cases with 0-8 hours per day:", zero_to_eight, "\n")

Number of cases with 0-8 hours per day: 10993 

cat("Number of cases with 4-8 hours per day:", four_to_eight, "\n")

Number of cases with 4-8 hours per day: 73 

cat("Number of cases with 8-14 hours per day:", eight_to_fourteen, "\n")

Number of cases with 8-14 hours per day: 21 

cat("Number of cases with more than 14 hours per day:", more_than_fourteen, "\n")

Number of cases with more than 14 hours per day: 33 

# Add text annotations to the plot
ggplot(data, aes(x = sports_activity_ss_read_hours_p / 7)) +
    geom_histogram(binwidth = 0.5, fill = "lightblue", color = "black") +
    scale_y_log10() +
    labs(title = "Distribution of Reading Hours per Day (before any filtering, including QC and NAs)", 
            x = "Reading Hours per Day", 
            y = "Count") +
    theme_minimal() +
    geom_vline(xintercept = c(8, 14), color = "black", linetype = "dashed", size = 1) +
    annotate("text", x = 4, y = 1200, label = paste("0-8 hours:", zero_to_eight), color = "darkgray") +
    annotate("text", x = 11, y = 1200, label = paste("8-14 hours:", eight_to_fourteen), color = "darkgray") +
    annotate("text", x = 18, y = 1200, label = paste(">14 hours:", more_than_fourteen), color = "darkgray")

# Combine the screentime data for weekday and weekend
# Change specific values in screentime columns
levels(data_filtered$screentime_wkdy_1)[levels(data_filtered$screentime_wkdy_1) == "0.25"] <- "15 minutes"
levels(data_filtered$screentime_wknd_7)[levels(data_filtered$screentime_wknd_7) == "< 30 minutes"] <- "15 minutes"

# Combine the screentime data for weekday and weekend
data_long <- tidyr::pivot_longer(data_filtered, cols = c(screentime_wkdy_1, screentime_wknd_7), 
                                 names_to = "day_type", values_to = "screentime_hours")

# Create a combined bar plot
ggplot(data_long, aes(x = factor(screentime_hours), fill = day_type)) +
  geom_bar(position = "dodge") +
  labs(title = "Distribution of Screentime on Weekdays and Weekends", 
       x = "Screentime (hours)", 
       y = "Count") +
  scale_fill_manual(values = c("lightgreen", "lightcoral"), 
                    labels = c("Weekday", "Weekend")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Filter cases where readtime is above 8 hours per day
data_filtered <- dplyr::filter(data_filtered, readtime <= 8)
cat("Number of lines after filtering by readtime: ", nrow(data_filtered), "\n")

Number of lines after filtering by readtime:  8127 

Relationships

# Function to compute R-squared and p-value
compute_regression_stats <- function(model) {
    r_squared <- summary(model)$r.squared
    p_value <- summary(model)$coefficients[2, 4]
    return(list(r_squared = r_squared, p_value = p_value))
}

Explore the relationship between reading hours and ADHD scores (from the ADHD CBCL DSM5 Scale (t-score))

# `cbcl_scr_dsm5_adhd_t` vs `sports_activity_ss_read_hours_p`
model <- lm(cbcl_scr_dsm5_adhd_t ~ readtime, data = data_filtered)
res = compute_regression_stats(model)
annotation <- paste("R^2: ", round(res$r_squared, 2), "\np-value: ", format.pval(res$p_value, digits = 2))
cat("Coefficients:\n")

Coefficients:

print(summary(model)$coefficients)

              Estimate Std. Error    t value     Pr(>|t|)
(Intercept) 53.3916355 0.08074427 661.243621 0.000000e+00
readtime    -0.4620706 0.08631384  -5.353377 8.868582e-08

# Plot
ggplot(data_filtered, aes(x = sports_activity_ss_read_hours_p, y = cbcl_scr_dsm5_adhd_t)) +
    geom_point(color = "blue", alpha = 0.5, position = position_jitter(width = 1, height = 1)) +
    labs(title = "Scatter plot of ADHD Scores vs Reading Hours (Filtered)",
         x = "Reading Hours per Week",
         y = "ADHD Scores") +
    theme_minimal() +
    geom_smooth(method = "lm", color = "red", se = FALSE) +
    annotate("text", x = 35, 
             y = 56, 
             label = annotation, 
             color = "black")

# `cbcl_scr_dsm5_adhd_t` vs `screentime`
model <- lm(cbcl_scr_dsm5_adhd_t ~ screentime, data = data_filtered)
res = compute_regression_stats(model)
annotation <- paste("R^2: ", round(res$r_squared, 2), "\np-value: ", format.pval(res$p_value, digits = 2))
cat("Coefficients:\n")

Coefficients:

print(summary(model)$coefficients)

              Estimate Std. Error    t value     Pr(>|t|)
(Intercept) 52.4515320 0.09671206 542.347380 0.000000e+00
screentime   0.5354055 0.06044013   8.858444 9.841849e-19

# Plot
ggplot(data_filtered, aes(x = screentime, y = cbcl_scr_dsm5_adhd_t)) +
    geom_point(color = "blue", alpha = 0.5, position = position_jitter(width = 0.08, height = 1)) +
    labs(title = "Scatter plot of ADHD Scores vs Screentime Hours (Filtered)",
         x = "Screentime Hours per Week",
         y = "ADHD Scores") +
    theme_minimal() +
    geom_smooth(method = "lm", color = "red", se = FALSE) +
    annotate("text", x = 3, 
            y = 57, 
            label = annotation, 
            color = "black")

Explore the NIH Toolbox Composite Scores

# Summary statistics for NIH Toolbox Scores
cat("Summary statistics for nihtbx_cryst_uncorrected: \n")

Summary statistics for nihtbx_cryst_uncorrected: 

summary(data_filtered$nihtbx_cryst_uncorrected)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  59.00   83.00   87.00   86.99   91.00  115.00     115 

cat("Summary statistics for nihtbx_fluidcomp_uncorrected: \n")

Summary statistics for nihtbx_fluidcomp_uncorrected: 

summary(data_filtered$nihtbx_fluidcomp_uncorrected)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  44.00   86.00   93.00   92.35   99.00  131.00     150 

cat("Summary statistics for nihtbx_totalcomp_uncorrected: \n")

Summary statistics for nihtbx_totalcomp_uncorrected: 

summary(data_filtered$nihtbx_totalcomp_uncorrected)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  46.00   82.00   88.00   87.06   93.00  117.00     154 

# Add a new column to categorize readtime as <4 hours or >=4 hours
data_filtered$readtime_category <- ifelse(data_filtered$readtime < 4, "<4 hours", ">=4 hours")

## Compute R-squared and p-values for all 6 regressions
# List to store results
regression_results <- list()

# Regression 1: nihtbx_cryst_uncorrected vs readtime for readtimes < 4 hours
model1 <- lm(nihtbx_cryst_uncorrected ~ readtime, data = dplyr::filter(data_filtered, readtime < 4))
regression_results[["Crystallized Cognition vs Reading Time (<4 hours)"]] <- compute_regression_stats(model1)

# Regression 2: nihtbx_cryst_uncorrected vs readtime for readtimes >= 4 hours
model2 <- lm(nihtbx_cryst_uncorrected ~ readtime, data = dplyr::filter(data_filtered, readtime >= 4))
regression_results[["Crystallized Cognition vs Reading Time (>=4 hours)"]] <- compute_regression_stats(model2)

# Regression 3: nihtbx_fluidcomp_uncorrected vs readtime for readtimes < 4 hours
model3 <- lm(nihtbx_fluidcomp_uncorrected ~ readtime, data = dplyr::filter(data_filtered, readtime < 4))
regression_results[["Fluid Cognition vs Reading Time (<4 hours)"]] <- compute_regression_stats(model3)

# Regression 4: nihtbx_fluidcomp_uncorrected vs readtime for readtimes >= 4 hours
model4 <- lm(nihtbx_fluidcomp_uncorrected ~ readtime, data = dplyr::filter(data_filtered, readtime >= 4))
regression_results[["Fluid Cognition vs Reading Time (>=4 hours)"]] <- compute_regression_stats(model4)

# Regression 5: nihtbx_totalcomp_uncorrected vs readtime for readtimes < 4 hours
model5 <- lm(nihtbx_totalcomp_uncorrected ~ readtime, data = dplyr::filter(data_filtered, readtime < 4))
regression_results[["Total Cognition vs Reading Time (<4 hours)"]] <- compute_regression_stats(model5)

# Regression 6: nihtbx_totalcomp_uncorrected vs readtime for readtimes >= 4 hours
model6 <- lm(nihtbx_totalcomp_uncorrected ~ readtime, data = dplyr::filter(data_filtered, readtime >= 4))
regression_results[["Total Cognition vs Reading Time (>=4 hours)"]] <- compute_regression_stats(model6)

# Print results
for (regression in names(regression_results)) {
    cat(regression, "\n")
    cat("R-squared: ", regression_results[[regression]]$r_squared, "\n")
    cat("p-value: ", regression_results[[regression]]$p_value, "\n\n")
}

Crystallized Cognition vs Reading Time (<4 hours) 
R-squared:  0.1256564 
p-value:  2.589591e-234 

Crystallized Cognition vs Reading Time (>=4 hours) 
R-squared:  0.02890613 
p-value:  0.2190304 

Fluid Cognition vs Reading Time (<4 hours) 
R-squared:  0.03680869 
p-value:  1.424086e-66 

Fluid Cognition vs Reading Time (>=4 hours) 
R-squared:  6.070349e-05 
p-value:  0.9558442 

Total Cognition vs Reading Time (<4 hours) 
R-squared:  0.09007422 
p-value:  1.511456e-164 

Total Cognition vs Reading Time (>=4 hours) 
R-squared:  0.00211991 
p-value:  0.7433841 

## Combine the three scatter plots into one plot with facets
# Prepare data for faceting
data_filtered_long <- data_filtered %>%
    tidyr::pivot_longer(cols = c(nihtbx_cryst_uncorrected, nihtbx_fluidcomp_uncorrected, nihtbx_totalcomp_uncorrected),
                        names_to = "variable", values_to = "score")

# Add regression results to the data for annotation
data_filtered_long <- data_filtered_long %>%
    dplyr::mutate(
        regression_label = dplyr::case_when(
            variable == "nihtbx_cryst_uncorrected" & readtime_category == "<4 hours" ~ paste("R^2: ", round(regression_results[["Crystallized Cognition vs Reading Time (<4 hours)"]]$r_squared, 3), "\np-value: ", format.pval(regression_results[["Crystallized Cognition vs Reading Time (<4 hours)"]]$p_value, digits = 2)),
            variable == "nihtbx_cryst_uncorrected" & readtime_category == ">=4 hours" ~ paste("R^2: ", round(regression_results[["Crystallized Cognition vs Reading Time (>=4 hours)"]]$r_squared, 3), "\np-value: ", format.pval(regression_results[["Crystallized Cognition vs Reading Time (>=4 hours)"]]$p_value, digits = 2)),
            variable == "nihtbx_fluidcomp_uncorrected" & readtime_category == "<4 hours" ~ paste("R^2: ", round(regression_results[["Fluid Cognition vs Reading Time (<4 hours)"]]$r_squared, 3), "\np-value: ", format.pval(regression_results[["Fluid Cognition vs Reading Time (<4 hours)"]]$p_value, digits = 2)),
            variable == "nihtbx_fluidcomp_uncorrected" & readtime_category == ">=4 hours" ~ paste("R^2: ", round(regression_results[["Fluid Cognition vs Reading Time (>=4 hours)"]]$r_squared, 3), "\np-value: ", format.pval(regression_results[["Fluid Cognition vs Reading Time (>=4 hours)"]]$p_value, digits = 2)),
            variable == "nihtbx_totalcomp_uncorrected" & readtime_category == "<4 hours" ~ paste("R^2: ", round(regression_results[["Total Cognition vs Reading Time (<4 hours)"]]$r_squared, 3), "\np-value: ", format.pval(regression_results[["Total Cognition vs Reading Time (<4 hours)"]]$p_value, digits = 2)),
            variable == "nihtbx_totalcomp_uncorrected" & readtime_category == ">=4 hours" ~ paste("R^2: ", round(regression_results[["Total Cognition vs Reading Time (>=4 hours)"]]$r_squared, 3), "\np-value: ", format.pval(regression_results[["Total Cognition vs Reading Time (>=4 hours)"]]$p_value, digits = 2))
        )
    )

# Plot with facets
combined_plot <- ggplot(data_filtered_long, aes(x = readtime, y = score)) +
    # Points with fill aesthetic
    geom_point(alpha = 0.2, position = position_jitter(width = 0.05, height = 1), 
               aes(fill = readtime_category), shape = 21, size = 2, stroke = 0) + 
    
    # Fit lines with color aesthetic
    geom_smooth(method = "lm", se = TRUE, size = 2, linetype = "solid",
                aes(color = readtime_category)) +
    
    # Separate scales for points (fill) and lines (color)
    scale_fill_manual(
        values = c("<4 hours" = "steelblue", ">=4 hours" = "darkseagreen") # Points' fill colors
    ) +
    scale_color_manual(
        values = c("<4 hours" = "darkblue", ">=4 hours" = "darkgreen")     # Lines' colors
    ) +
    
    # Labels
    labs(
        # title = "Scatter plots of NIH Toolbox un-corrected Cognition Scores vs Reading Hours",
        plot.title = element_text(hjust = 0.5),
        x = "Reading Hours per Day",
        y = "Raw Score",
    ) +
    
    # Facets
    facet_wrap(~ variable, scales = "free_x", ncol = 3, 
               labeller = as_labeller(c(
                   nihtbx_cryst_uncorrected = "Crystallized", 
                   nihtbx_fluidcomp_uncorrected = "Fluid", 
                   nihtbx_totalcomp_uncorrected = "Total"
               ))) +
    
    # Y-axis limits
    ylim(45, 130) +
    
    # Theme adjustments
    theme_minimal() +
    theme(
        legend.position = "none",
        aspect.ratio = 0.5,
        plot.title = element_text(size = 20),
        axis.title.x = element_text(size = 16),
        axis.title.y = element_text(size = 16),
        axis.text = element_text(size = 14),
        strip.text = element_text(size = 18)
    ) +
    
    # Add regression results as text annotations
    geom_text(data = data_filtered_long %>% dplyr::filter(readtime_category == "<4 hours"), 
              aes(x = 2, y = 50, label = regression_label), 
              color = "black", size = 3, parse = FALSE) +
    geom_text(data = data_filtered_long %>% dplyr::filter(readtime_category == ">=4 hours"), 
              aes(x = 6, y = 50, label = regression_label), 
              color = "black", size = 3, parse = FALSE)

print(combined_plot)
# Save the combined plot to the figures directory
output_file <- file.path(figures_dir, "combined_uncorrected_cognition_scores_vs_reading_hours2.png")
ggsave(output_file, plot = combined_plot, width = 20, height = 6, bg = "white")

cat("Combined plot saved to:", output_file, "\n")

Combined plot saved to: ../../figures/combined_uncorrected_cognition_scores_vs_reading_hours2.png 

# Scatter plot: `nihtbx_totalcomp_uncorrected` vs `readtime`
ggplot(data_filtered, aes(x = cbcl_scr_dsm5_adhd_t, y = nihtbx_totalcomp_uncorrected)) +
    geom_point(alpha = 0.5, position = position_jitter(width = 0.05, height = 0.4)) +
    labs(title = "Scatter plot of Total Cognition Scores vs ADHD t-scores",
         x = "ADHD t-scores",
         y = "Total Cognition Scores") +
    theme_minimal() +
    geom_smooth(method = "lm", se = TRUE)

Export design matrix for FEMA analysis

Here, we need to export the matrix in a specific format, with src_subject_id, eventname, rel_family_id, age, other predictor variables. We will use the data_subset dataframe for this purpose. We also need to dummy encode the categorical variables, check for rank deficiency, remove the redundant columns, add the intercept column, and save the matrix to a tab-separated file.

#install.packages("psych")
#install.packages("ordinal")
#install.packages("pracma")
source("../../code/matlab/cmig_tools/cmig_tools_utils/r/makeDesign.R")

vars_of_interest = c("readtime", "screentime")

outfile <- "../../data/derived/design_matrix_readtime+screentime.txt"
time <- c("baseline_year_1_arm_1")
contvar <- c("interview_age", vars_of_interest, paste0("PC", 1:10))
catvar <- c("sex", "abcd_site", "married.bl", "household.income.bl", "high.educ.bl", "hisp", "mri_info_device.serial.number")
demean <- FALSE

# check that all variables are in data_subset
for (var in c(contvar, catvar)) {
  if (!var %in% colnames(data_subset)) {
    stop(paste("Variable", var, "not found in data_subset"))
  }
}

# Call the makeDesign function
design_matrix <- makeDesign(
  nda = data_subset,
  outfile = outfile,
  time = time,
  contvar = contvar,
  catvar = catvar,
  demean = demean
)

design is column rank deficient so dropping 5 coef